User competency assessment method and device, electronic equipment and storage medium

Abstract

The invention provides a user competency assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: responding to a query request input by a user, and checking an account of the user; after checking succeeds, multi-dimensional information of the user to be checked in the query request is queried; based on the queried multi-dimensional information, evaluating the score of each preset index in a plurality of preset indexes; based on scores of all preset indexes, determining a talent competency index score of the to-be-checked user; and according to the talent competency index score, determining the talent competency level of the to-be-checked user. According to the method and the device, the score of each preset index in the plurality of preset indexes is evaluated based on the inquired multi-dimensional information of the to-be-inquired user, and the talent competency level of the to-be-inquired user is determined according to the sum of the scores of all the talent competency indexes, so that an institution is helped to select talents, for example, nursing workers are screened for an old-age service institution, and the user experience is improved. Therefore, the overall level of the old-age care service is improved.

Description

Technical Field

[0001] This application relates to the field of big data processing technology, and in particular to a user competence assessment method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the rapid development of the internet and the improvement of overall living standards, the number of elderly care service institutions continues to grow. However, many of these institutions lack a systematic and standardized screening mechanism for recruiting caregivers, making it difficult to effectively verify their professional abilities, service quality, and relevant background information. This leads to deficiencies in the personnel management system for elderly care services, resulting in inconsistent quality of caregiver services. This not only poses operational risks but may also affect the service experience and safety of the elderly residents. Summary of the Invention

[0003] This application provides a user competence assessment method, device, electronic device, and storage medium to at least address the problems in related technologies where the lack of a systematic and standardized screening mechanism in the recruitment of caregivers makes it difficult to effectively verify their professional abilities, service quality, and relevant background information, leading to poor elderly care service experiences and safety issues. The technical solution of this application is as follows: According to a first aspect of the embodiments of this application, a user competence assessment method is provided, comprising: In response to a query request entered by the user, the user's account is verified; After successful verification, query the multi-dimensional information of the user to be queried in the query request; Based on the retrieved multi-dimensional information, evaluate the score of each preset indicator among multiple preset indicators; Based on the scores of all preset indicators, the talent competency index score of the user to be investigated is determined. The talent competency level of the user to be investigated is determined based on the talent competency index score.

[0004] Optionally, the step of verifying the user's account in response to a user-input query request includes: Get the query request input by the user; The user's permissions and account balance are authenticated; The query fee will be deducted after authentication is successful; and After the query is completed, a blockchain certificate is generated based on the query results.

[0005] Optionally, after successful verification, querying the multi-dimensional information of the user to be queried in the query request includes: After successful verification, the multi-dimensional information of the user to be searched in the query request is queried through the interface of the federated query gateway. The multi-dimensional information includes: family background, social security participation, professional qualifications, education level, health status and whether there is a criminal record.

[0006] Optionally, the method further includes: pre-establishing the interface of the federated query gateway in the following manner: A gateway for a unified application programming interface (API); Carry a one-time identity token (such as a Scope JWT) with each query, and Each original record retrieved will be converted into a corresponding integer score via ETL; For metrics that require cross-source aggregation, the homomorphic encryption Paillier method is used for processing; When query results reveal individual characteristics, Laplace noise is automatically injected according to the privacy budget. Calculate a verifiable delay function (VDF) workload proof for each query; Write the query serial number, the fraction hash returned by the data source, and the timestamp into the consortium blockchain.

[0007] Optionally, the step of evaluating the score of each preset indicator among multiple preset indicators based on the multi-dimensional information includes: The scoring rules for the preset indicators are obtained, including: health dimension indicators, background dimension indicators, credit dimension indicators and basic qualification dimension indicators. Based on the multi-dimensional information, and according to the scoring rules of the preset indicators, the health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators are evaluated respectively, and the corresponding scores for the health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators are obtained.

[0008] Optionally, determining the talent competency index score of the user to be investigated based on the scores of all preset indicators includes: The sum of scores for all preset indicators is calculated according to the set weights to obtain the talent competency index score of the user to be investigated.

[0009] Optionally, determining the talent competency level of the user to be queried based on the talent competency index score includes: Obtain preset talent competency index levels; Based on the preset talent competency index level and the talent competency index score, the talent competency level corresponding to the user to be searched is determined.

[0010] Optionally, in the process of querying the multi-dimensional information of the user to be queried in the query request, the method further includes: Obtain the user's query behavior data during the query process; The user's query behavior data is detected; When abnormal user behavior is detected, secondary verification is triggered; If the secondary verification fails, the user's query privileges will be frozen.

[0011] Optionally, after successful verification, the method further includes retrieving the multi-dimensional information of the user to be queried from the query request, wherein the method includes: Identify sensitive data in the multi-dimensional information of the user to be queried; The identified sensitive data is anonymized to obtain multi-dimensional information.

[0012] According to a second aspect of the embodiments of this application, a user competence assessment device is provided, comprising: The verification module is used to verify the user's account in response to a query request input by the user; The first query module is used to query the multi-dimensional information of the user to be queried in the query request after the verification is successful; The evaluation module is used to evaluate the score of each preset indicator among multiple preset indicators based on the queried multi-dimensional information. The first determining module is used to determine the talent competency index score of the user to be investigated based on the scores of all preset indicators. The second determining module is used to determine the talent competency level of the user to be checked based on the talent competency index score.

[0013] Optionally, the verification module includes: The request retrieval module is used to retrieve the query request input by the user; The authentication module is used to authenticate the user's permissions and account balance; The deduction module is used to deduct the query fee after successful authentication; and The generation module is used to generate blockchain evidence based on the query results after the query module completes the query.

[0014] Optionally, the query module is specifically used to query the multi-dimensional information of the user to be queried in the query request through the interface of the federated query gateway after the verification is successful. The multi-dimensional information includes: family background, social security participation, professional qualifications, education level, health status and whether there is a criminal record.

[0015] Optionally, the method further includes: a creation module for pre-creating a federated query gateway.

[0016] Optionally, the creation module includes: A unified module, used as a gateway to unify application programming interfaces (APIs); Add a module to carry a one-time identity token (such as a Scope JWT) for each query, and The transformation module is used to convert each raw record retrieved by the query into a corresponding integer score via ETL. The encryption processing module is used to process indicators that require cross-source aggregation using the homomorphic encryption Paillier method. The privacy computing module is used to automatically inject Laplace noise according to a privacy budget when the query results contain individual characteristics (such as rare infectious diseases); The calculation module is used to calculate a verifiable delay function (VDF) proof of work for each query; The storage module is used to write the query serial number, the fractional hash returned by the data source, and the timestamp into the consortium blockchain.

[0017] Optionally, the evaluation module includes: The scoring rule acquisition module is used to acquire the scoring rules for preset indicators, which include: health dimension indicators, background dimension indicators, credit dimension indicators and basic qualification dimension indicators. The indicator scoring module is used to evaluate the health dimension indicator, background dimension indicator, credit dimension indicator and basic qualification dimension indicator respectively based on the multi-dimensional information and according to the preset indicator scoring rules, so as to obtain the corresponding health dimension indicator score, background dimension indicator score, credit dimension indicator score and basic qualification dimension indicator score.

[0018] Optionally, the first determining module is specifically used to calculate the sum of scores for all preset indicators according to set weights to obtain the talent competency index score of the user to be investigated.

[0019] Optionally, the second determining module includes: The indicator rule acquisition module is used to acquire preset talent competency indicator levels; The level determination module is used to determine the talent competency level of the user to be checked based on the preset talent competency index level and the talent competency index score.

[0020] Optionally, the device further includes: The second query module is used to obtain the user's query behavior data during the query process while the first query module is querying the multi-dimensional information of the user to be queried in the query request. The behavior verification module is used to verify the user's query behavior data; The triggering module is used to trigger secondary verification when abnormal behavior of the user is detected; The freeze module is used to freeze the user's query permissions when the secondary verification fails.

[0021] Optionally, after determining the talent competency level corresponding to the user to be queried, the device further includes: The sensitive data identification module is used to identify sensitive data in the multi-dimensional information of the user to be queried. The desensitization module is used to desensitize the identified sensitive data to obtain desensitized multi-dimensional information.

[0022] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising: It includes a processor, a memory; and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the user competence assessment method as described above.

[0023] According to a fourth aspect of the embodiments of this application, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor of an electronic device, implement the steps of the user competence assessment method as described above.

[0024] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program or instructions that, when executed by a processor of an electronic device, implement the steps of the user competence assessment method as described above.

[0025] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: In this embodiment, in response to a user's query request, the user's account is verified. Upon successful verification, multi-dimensional information about the user in the query request is retrieved. Based on the retrieved multi-dimensional information, the score of each of multiple preset indicators is evaluated. Based on the scores of all preset indicators, the talent competency index score of the user is determined. According to the talent competency index score, the talent competency level of the user is determined. In other words, in this embodiment, based on the retrieved multi-dimensional information of the user, the score of each of multiple preset indicators is evaluated, and the talent competency level of the user is determined by the sum of all talent competency index scores. This helps institutions select talent, such as screening caregivers for elderly care service institutions, thereby improving the overall level of elderly care services.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0027] The accompanying drawings, incorporated in and forming part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. They do not constitute an undue limitation of this application. To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a user competency assessment method provided in an embodiment of this application.

[0029] Figure 2 This is a schematic diagram illustrating the application of a user competency assessment method provided in an embodiment of this application.

[0030] Figure 3 This is a block diagram of a user competency assessment device provided in an embodiment of this application.

[0031] Figure 4 This is a block diagram of a verification module provided in an embodiment of this application.

[0032] Figure 5 This is a block diagram of an evaluation module provided in an embodiment of this application.

[0033] Figure 6 This is a second determining module provided in the embodiments of this application.

[0034] Figure 7 This is another block diagram of a user competency assessment device provided in the embodiments of this application.

[0035] Figure 8 This is a block diagram of an electronic device provided in an embodiment of this application.

[0036] Figure 9 This is a block diagram of an apparatus for assessing user competence, provided in an embodiment of this application. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0038] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] Figure 1 This is a flowchart of a user competency assessment method provided in an embodiment of this application, such as... Figure 1 As shown, this user competency assessment method includes the following steps: Step 101: In response to the query request entered by the user, verify the user's account.

[0040] Step 102: After successful verification, query the multi-dimensional information of the user to be queried in the query request.

[0041] Step 103: Based on the retrieved multi-dimensional information, evaluate the score of each preset indicator among multiple preset indicators.

[0042] Step 104: Based on the scores of all preset indicators, determine the talent competency index score of the user to be investigated.

[0043] Step 105: Determine the talent competency level of the user to be investigated based on the talent competency index score.

[0044] The user competency assessment method described in this application can be applied to terminals, servers, etc., without limitation. The terminal implementation device can be an electronic device such as a smartphone, laptop, tablet, desktop computer, personal digital assistant (PDA), and wearable device. The server can be an independent server, a server cluster, or a server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, or big data and artificial intelligence platforms, etc., without limitation.

[0045] The following is combined with Figure 1 The specific implementation steps of a user competency assessment method provided in this application embodiment will be described in detail.

[0046] In step 101, in response to a query request input by the user, the user's account is verified.

[0047] This step includes: 1) obtaining the query request input by the user; wherein, the query request includes the user's identity identifier and the identity identifier of the user to be queried, etc. Of course, it may also include other information, which is not limited in this embodiment.

[0048] 2) Authenticate the user's permissions and account balance; In this step, the user's permissions are first authenticated based on the user's identity.

[0049] User permissions serve as the system's zero-trust security gateway, integrating identity authentication, multi-factor authentication, fine-grained authorization, token lifecycle management, real-time risk control, and end-to-end auditing. Individual users require five layers of verification: name, ID number, mobile phone number, liveness detection (e.g., facial recognition), and mobile TEE private key signature. This can be adapted to specific needs, and this implementation does not impose restrictions. Enterprises must complete both random bank account transfers and facial recognition verification by the legal representative to ensure "identity verification" and "institutional authenticity." After successful permission verification, the system (or platform) uses a permission management module (e.g., a hybrid model of RBAC and ABAC) to issue only one-time, field-level identity tokens (e.g., ScopeJWT) with a 5-minute expiration time (this is just an example; other times are possible, and this implementation does not impose restrictions). User private keys never leave the mobile phone and cannot be reused on the platform. Authorization hashes are written to the blockchain in real-time, and users can revoke authorization with a single click on the app, effective across the entire network within one minute. It incorporates national cryptographic algorithms for end-to-end encryption and implements access control for API interfaces, achieving a zero identity spoofing rate and a token leakage impact window of ≤3 minutes (this is just an example; other timeframes, such as 5 minutes, are also possible). It also outputs tamper-proof audit logs, providing a trusted foundation for subsequent big data queries and privacy computing.

[0050] Role-Based Access Control (RBAC) is an access control model that manages permissions through a user-role-permission mapping. This application's embodiment simplifies user and permission management through RBAC, making it easy to extend and maintain.

[0051] Among them, the authorization model (ABAC, Attribute-Based Access Control) is an attribute-based access control model that uses the attributes of relevant entities (such as subjects, objects, and environments) as the basis for authorization to study how to perform access control. In the ABAC model, access decisions are based on the attributes of the requester and the resource, which makes ABAC sufficiently flexible and scalable, while also enabling secure anonymous access.

[0052] Secondly, after user authorization is approved, the user's account balance is verified.

[0053] In this step, after the user's authorization is verified, it is determined whether the user's account has a balance and whether the balance is sufficient for this deduction. If there is a balance and it is sufficient for this deduction, step 3) is executed directly, that is, the query fee is deducted; otherwise, the verification is deemed to have failed and the user is prompted to recharge in time.

[0054] Of course, in this embodiment, the query fee to be deducted can be frozen first, and then deducted after the query is completed.

[0055] 3) The query fee will be deducted after the authentication is successful.

[0056] 4) After the query is completed, a blockchain certificate is generated based on the query results.

[0057] This embodiment proposes a charging mechanism for querying sensitive data on the platform, balancing the needs of both tradition and innovation. It supports conventional user recharge, balance inquiry, and bill reconciliation functions, while also introducing a blockchain prepaid smart contract mechanism: for each sensitive data query, the system automatically freezes a preset query fee (e.g., 0.5 yuan, 1 yuan, 3 yuan, or 5 yuan, which can be adjusted according to actual needs, such as to 10 yuan). After the query is completed, the hash value of the result is stored on the blockchain in real time to ensure that the data call process is open, transparent, and tamper-proof, preventing fee disputes and subsequent repudiation from the source, and establishing a credible and traceable financial interaction mechanism between the platform operator and the elderly care institution.

[0058] In step 102, after the verification is successful, the multi-dimensional information of the user to be queried in the query request is retrieved.

[0059] In this step, after the verification is successful, the multi-dimensional information of the user to be searched in the query request can be queried through the interface of the federated query gateway. The multi-dimensional information includes: family background, social security participation, professional qualifications, education level, health status and whether there is a criminal record.

[0060] Specifically, based on the user identity identifier of the user to be queried in the query request, the interface of the federated query gateway is used to query the social security participation database, the health and health records database, the public security criminal record database, the civil affairs assistance and subsidy database, and the Ministry of Human Resources and Social Security vocational skills training and certificate database to obtain the multi-dimensional information of the user to be queried. The multi-dimensional information includes: family background, social security participation, professional qualifications, education level, health status, and whether there is a criminal record.

[0061] The interface of the federated query gateway can be established in the following ways: a gateway for a unified application programming interface (API); carrying a one-time identity token (such as a Scope JWT) for each query, and converting each original record found in the query into a corresponding integer score, such as an integer score between 0 and 100, via ETL; processing indicators that need to be aggregated across sources using the homomorphic encryption Paillier method; automatically injecting Laplace noise according to a privacy budget when the query results have individual characteristics (such as rare infectious diseases); calculating a verifiable delay function (VDF) proof-of-work for each query; and writing the query serial number, the score hash returned by the data source, and the timestamp into the consortium blockchain.

[0062] Among them, 1) Federated Query Gateway: Unified Application Programming Interface (API) gateway, REST / GRPC dual protocol; national cryptographic TLS 1.3 bidirectional certificate channel, SM2 key negotiation + SM4 transmission encryption; query requests carry one-time Scope JWT, and the gateway automatically parses the required fields and geographical range; each data source only returns "ciphertext fractional packets", and the platform side cannot deduce the original plaintext.

[0063] 2) Fractionalization pipeline: Each raw record is converted into an integer fraction of 0–100 via local ETL and accompanied by a 256-bit salt hash for subsequent verifiability comparison.

[0064] ETL (Extract-Transform-Load) is the process of extracting, transforming, and loading large amounts of raw data into a target data warehouse.

[0065] 3) "Homomorphic encryption + zero-knowledge proof" double insurance: Paillier homomorphic addition is used for indicators that need to be aggregated across sources, and the platform can directly sum them in the ciphertext space; zk-SNARK is used for Boolean indicators (such as whether there is a crime or not) to prove that "the plaintext satisfies a certain logic" without revealing the plaintext.

[0066] The Paillier Pascal algorithm is a public-key encryption algorithm based on the difficult problem of compound residue classes. A representative of public-key encryption systems, the Paillier algorithm was invented by Paillier in 1999. It is a homomorphic encryption algorithm; homomorphic encryption only satisfies additive homomorphism, and along with zero-knowledge proofs, it is a standard method for protecting transaction privacy.

[0067] 4) Differential privacy noise dynamic injection: When the returned result may expose individual characteristics (such as rare infectious diseases), the gateway automatically injects Laplace noise with a privacy budget of ε≤1 to ensure that attackers cannot reverse-target specific individuals even if they have background knowledge.

[0068] 5) Verifiable Delay Function (VDF) prevents replay attacks. Each query requires the calculation of a VDF proof-of-work, which takes about 200 ms, thus preventing high-frequency credential stuffing and replay attacks.

[0069] 6) Blockchain Receipt Compliance and Liability Disclaimer: The Federated Query Gateway writes "query serial number + score hash returned by the data source + timestamp" into the consortium's blockchain. The data provider receives a receipt on the blockchain in real time to meet the requirement of "recording data processing activities" so that it can prove its innocence in the event of a dispute.

[0070] In other words, in this step, data resource querying is the core production element layer of the system, including data entry and data governance. Data entry is responsible for transforming various types of raw data into standardized data that is computable and can be compliantly circulated. For privacy data, privacy-preserving algorithms are typically used to calculate criminal records (if a criminal record is found, a privacy algorithm is used to calculate the criminal record, and the result only provides the corresponding score, for example, a total score of 10, giving 7 points, etc. The privacy algorithm can derive a model to convert criminal records into corresponding scores, etc.).

[0071] This application proposes a unified exit point using a "federated query gateway." Through the gateway's interface, users can query authoritative databases such as the social security participant database, health and wellness records database, public security crime database, civil affairs assistance and subsidy database, and the Ministry of Human Resources and Social Security's vocational skills training and certificate database to obtain multi-dimensional information (i.e., multi-dimensional data). Furthermore, based on this multi-dimensional information, the scores of each preset indicator among multiple preset indicators can be evaluated. This multi-dimensional information is input into the evaluation model, allowing it to output standardized and encrypted scores for multiple preset indicators. The entire process ensures that "raw data remains within the domain, calculation results are verifiable, and privacy risks are traceable."

[0072] In step 103, based on the retrieved multi-dimensional information, the score of each preset indicator among multiple preset indicators is evaluated.

[0073] In this step, firstly, the scoring rules for preset indicators are obtained. These preset indicators include: health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators. Secondly, based on the multi-dimensional information, and according to the scoring rules for the preset indicators, the health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators are evaluated respectively, resulting in corresponding scores for the health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators.

[0074] It should be noted that, in response to different industry needs, the preset indicators can be adaptively increased or decreased according to actual application requirements. This embodiment does not impose any restrictions on this, and all such changes are within the scope of protection of this embodiment.

[0075] The following embodiment of this application uses the recruitment of caregivers for elderly care services as an example to illustrate the required multiple preset indicators and the scoring rules for these multiple preset indicators. In this embodiment, the health dimension indicator, background dimension indicator, credit dimension indicator and basic qualification dimension indicator and their corresponding scoring rules are used as examples.

[0076] 1) Preset indicators - health dimension indicators, whose weight can be set to 40%, with a full score of 40 points, as shown in Table 1, including: dimension, indicator items, weight ratio and scoring rules.

[0077] Table 1

[0078] It should be noted that the indicators in Table 1 are based on health certificates, diseases, past medical history, history of infectious diseases, and medication use, but in actual application, they are not limited to these.

[0079] 2) Preset indicators - background dimension indicators, whose weight can be set to 25%, with a full score of 25 points, as shown in Table 2, including: dimension, indicator item, weight ratio and scoring rules.

[0080] Table 2

[0081] It should be noted that the indicators in Table 2 are based on criminal records and civil disputes, but in practice, they are not limited to these.

[0082] 3) Preset indicators - credit dimension indicators, whose weight can be set to 15%, with a full score of 15 points, as shown in Table 3, including: dimension, indicator item, weight ratio and scoring rules.

[0083] Table 3

[0084] It should be noted that the indicators in Table 3 are based on credit expectation, but in practice, they are not limited to this.

[0085] 4) Preset indicators - basic qualification dimension indicators, whose weight can be set to 20%, with a full score of 20 points, as shown in Table 4, including: dimension, indicator item, weight ratio and scoring rules.

[0086] Table 4

[0087] It should be noted that the indicators in Table 4 are based on social security and education as examples, but in practice, they are not limited to these.

[0088] In other words, this step utilizes an evaluation model to objectively assess and score various pre-defined indicators of the user. By collecting multi-dimensional information about the user to be investigated, such as family background, social security participation, professional qualifications, education level, health status, and criminal record, and inputting this information into the evaluation model, the model uses pre-set evaluation rules and algorithms to meticulously assess each pre-defined indicator (i.e., according to the pre-defined indicators, it calculates the score for each indicator of the user and records the corresponding score). This process aims to comprehensively understand the personal and social characteristics of the user to be investigated and generate scores for the corresponding pre-defined indicators based on this information, providing a strong basis for subsequent decision-making and support.

[0089] In step 104, the talent competency index score of the user to be investigated is determined based on the scores of all preset indicators.

[0090] In this step, the scores of all preset indicators are calculated according to the set weights to obtain the talent competency index score of the user to be investigated. For example, the scores of all indicator items (e.g., 10 items) of each of the above preset indicators are added together to obtain the final talent competency index score of the user to be investigated.

[0091] In step 105, the talent competency level of the user to be investigated is determined according to the talent competency index score.

[0092] In this step, a preset talent competency index level is obtained; based on the talent competency index score, the talent competency level corresponding to the user to be checked is determined according to the preset talent competency index level.

[0093] The talent competency index levels can include: excellent, good, qualified, unqualified, etc., as well as the score range and description of each level, as shown in Table 5.

[0094] Table 5

[0095] Based on Table 5, according to the preset talent competency index levels, the talent competency level corresponding to the user to be queried is output according to the talent competency index score. It should be noted that the score ranges corresponding to each level in Table 5 can be adaptively adjusted according to actual needs, and this embodiment does not impose any restrictions.

[0096] In other words, the competency assessment in this step aims to comprehensively evaluate an individual's ability to perform within a specific industry. This can be achieved using industry-specific algorithmic models, combined with scores from various pre-defined indicators (such as family background, social security record, professional qualifications, education level, health status, and legal compliance) for comprehensive analysis and scoring. This process accurately assesses whether an individual meets the requirements of a specific position, thus providing a scientific basis for recruitment decisions and individual career development.

[0097] In this embodiment, in response to a user's query request, the user's account is verified. Upon successful verification, multi-dimensional information about the user in the query request is retrieved. Based on the retrieved multi-dimensional information, the score of each of multiple preset indicators is evaluated. Based on the scores of all preset indicators, the talent competency index score of the user is determined. According to the talent competency index score, the talent competency level of the user is determined. In other words, in this embodiment, based on the retrieved multi-dimensional information of the user, the score of each of multiple preset indicators is evaluated, and the talent competency level of the user is determined by the sum of all talent competency index scores. This helps institutions select talent, such as screening caregivers for elderly care service institutions, thereby improving the overall level of elderly care services.

[0098] Optionally, in another embodiment, based on the above embodiment, the method may further include: acquiring the user's query behavior data during the query process; detecting the user's query behavior data; triggering secondary verification when abnormal user behavior is detected; and freezing the user's query permissions when secondary verification fails.

[0099] In other words, in this embodiment, the user behavior analysis module acts as a real-time risk control sentinel in the system, collecting and analyzing user activity data from the log system. It retains traditional blacklist and whitelist rules such as UA, IP, and time period, while also introducing the GraphSAGE abnormal behavior graph engine: by converting account login sequences and authorization time sequences into graph nodes and edges, it aggregates highly similar account clusters in real time, enabling second-level identification of covert attacks such as "group authorization" and "bulk account farming." Once abnormal group activity is detected, it automatically triggers secondary face recognition or freezes query permissions, nipping group fraud risks in the bud before data queries. Furthermore, by collecting user activity data, it establishes baselines for normal user behavior patterns and preferences. By monitoring deviations between real-time user activity and these baselines, it helps detect abnormal behavior and identify potential security threats and unauthorized access attempts.

[0100] Optionally, in another embodiment, based on the above embodiment, after successful verification, the method further includes: identifying sensitive data in the multi-dimensional information of the user to be queried in the query request; and desensitizing the identified sensitive data to obtain desensitized multi-dimensional information.

[0101] This step requires the identification and desensitization of sensitive data in multi-dimensional information, specifically including sensitive data identification rule management and desensitization algorithm management.

[0102] Among these, sensitive data identification rule management automatically identifies sensitive information in data, such as personal identification information, financial records, and health data. Through preset rules and pattern matching technology, it ensures accurate identification of data elements requiring protection.

[0103] Data anonymization algorithm management involves applying appropriate anonymization algorithms to identified sensitive data. Various anonymization methods exist, including replacement, masking, and encryption, to ensure that data can still be used for analysis and processing without disclosing sensitive information.

[0104] One type of sensitive data desensitization process includes: 1) Establish sensitive data identification rules; the system will have some common de-identification rules pre-built in, such as regular expression recognition rules for ID card numbers, mobile phone numbers, email addresses, telephone numbers, academic certificate numbers, etc. De-identification rules can also be added dynamically, etc., which are not limited in this embodiment. 2) Establish de-identification algorithm management: The system will have a set of common algorithms built-in. For example: hash de-identification, character masking, keyword replacement, and algorithms such as RSA and AES, etc.

[0105] 3) Establish a desensitization strategy template; select sensitive identification rules and desensitization algorithms. The system will match sensitive data according to the regular expressions in the identification rules. When sensitive data is identified, the system will dynamically desensitize the sensitive data according to the selected desensitization algorithm.

[0106] 4) Configure anonymization strategy templates for APIs; an anonymization strategy template needs to be selected for the corresponding interface. All interfaces can use the same strategy template, or different anonymization strategy templates can be created for different interfaces. This embodiment does not impose any restrictions.

[0107] 5) When the API interface is accessed, the returned data will be identified as sensitive data according to the desensitization strategy template corresponding to the API, and then the corresponding algorithm will be used to desensitize the sensitive data.

[0108] In this embodiment, during the process of querying multidimensional user information, user privacy is protected, and their work experience, health status, and criminal record are verified while avoiding privacy leaks. This ensures service records are maintained, facilitates service supervision, and provides data-driven service standards.

[0109] Optionally, in another embodiment, based on the above embodiments, the step may further include: recording all operations of the user competency assessment in the access log for subsequent verification.

[0110] In this step, all user competency assessment operations can be recorded in the access log to facilitate access log management of all access operations. The aim is to monitor system usage and improve security, performance, and user experience by analyzing access patterns, ensuring that every access activity is accurately recorded, thereby providing necessary information for auditing, troubleshooting, and compliance checks.

[0111] Please also see Figure 2 This diagram illustrates an application example of a user competency assessment method provided in this application. The example uses the recruitment of caregivers (i.e., the users to be assessed) by a service-oriented elderly care platform (hereinafter referred to as the platform). However, in practical applications, other talents can be recruited as needed; this embodiment does not impose limitations. Specifically, it includes: Step 201: The user logs into the elderly care service platform, and the platform verifies the user's permissions; Users in this step can be individuals or employees of enterprises and institutions. They log in to the elderly care service platform through personal or corporate accounts. If logging in as an individual, the user must pass five verifications: name, ID number, mobile phone number, liveness detection, and mobile TEE private key signature. If logging in as an employee of an enterprise or institution, the user needs to complete a random payment to a corporate account and a facial recognition verification of the legal representative to ensure that the identity of the person matches the identification and that the institution is genuine.

[0112] Step 202: After successful verification, the elderly care platform receives a query request initiated by the user, which includes the identity identifier of the user to be queried.

[0113] The identity information of the user to be searched may include: mobile phone number, ID card number, personal name, etc.

[0114] In this step, after successfully logging into the elderly care service platform, browsing recruitment information is free. However, searching for individuals (such as caregivers) will incur a per-use charge. Before charging per use, the system will check if the user's balance is sufficient to cover the cost. If not, the user will be prompted to top up their account.

[0115] Step 203: Determine if the user's account balance is sufficient for this deduction; if yes, proceed to step 204; otherwise, proceed to step 214: prompt the user to recharge in time and return to step 203. In this embodiment, in addition to supporting conventional user recharge, balance inquiry, and bill reconciliation functions, a blockchain prepaid smart contract mechanism is introduced: each time a sensitive data query is called, the system automatically freezes or deducts a certain fee, and after the query is completed, the hash value of the result is uploaded to the blockchain in real time for evidence storage, ensuring that the data call process is open, transparent and tamper-proof, preventing fee disputes and subsequent repudiation from the source, and establishing a credible and traceable financial interaction mechanism between the platform operator and the elderly care institution.

[0116] Step 204: Deduct the query fee for this query.

[0117] In this step, the fee can be deducted directly, or the fee can be frozen first and deducted after the user completes the query. The result hash is then recorded on the blockchain to prevent subsequent repudiation.

[0118] Step 205: Generate blockchain evidence based on the deduction results.

[0119] Step 206: Query the multi-dimensional information of the user to be queried in the query request through the federated query gateway; In this step, the elderly care service platform queries various data sources through a federated query gateway, returning only encrypted scores; the original data remains within the domain, ensuring compliance and exemption from liability. For detailed process information, please refer to the corresponding implementation examples above, which will not be repeated here.

[0120] Step 207: Obtain user behavior data and analyze the user behavior data.

[0121] In this step, the elderly care service platform analyzes user calls and behaviors to avoid issues such as unauthorized access by groups.

[0122] Step 208: Verify the analysis results to determine if the user's behavior is abnormal. If the behavior is abnormal, a second verification is required. Proceed to step 209. If the behavior is normal, the verification is passed. Proceed to step 210.

[0123] Step 209: Determine whether the verification via SMS or email was successful. If yes, proceed to step 210; otherwise, proceed to step 211.

[0124] Step 210: Based on the retrieved multi-dimensional information, evaluate the score of each preset indicator among multiple preset indicators.

[0125] The specific evaluation process in this step is implemented according to the corresponding embodiments described above, and will not be repeated here.

[0126] Step 211: Based on the scores of all preset indicators, determine the talent competency index score of the user to be investigated.

[0127] In this step, talent competency aims to comprehensively evaluate an individual's ability to perform within a specific industry. It employs industry-specific algorithmic models, combining scores from various indicators (such as family background, social security record, professional qualifications, education level, health status, and legal compliance) for comprehensive analysis and scoring. This process helps accurately assess whether an individual meets the requirements of a specific position, thus providing a scientific basis for recruitment decisions and individual career development.

[0128] Step 212: Determine the talent competency level of the user to be investigated based on the talent competency index score.

[0129] In this step, the talent competency level of the user to be checked is determined based on the pre-defined talent competency index score.

[0130] Step 213: Identify and de-sensitize sensitive data in the results of the talent competency level of the user to be checked, and obtain the de-sensitized talent competency level.

[0131] This step involves two core functions: sensitive data identification and desensitization.

[0132] Sensitive data identification rule management: Its function is to automatically identify sensitive information in data, such as personal identification information, financial records, health data, etc., and to ensure accurate identification of data elements that need protection through preset rules and pattern matching technology.

[0133] Data anonymization algorithm management: Its function is to apply appropriate data anonymization algorithms to identified sensitive data. Various data anonymization methods exist, such as replacement, masking, and encryption, to ensure that data can still be used for analysis and processing without disclosing sensitive information.

[0134] Furthermore, in this embodiment of the application, in querying the multi-dimensional information of the user to be queried in the query request, the method may further include: identifying sensitive data in the multi-dimensional information of the user to be queried; and desensitizing the identified sensitive data to obtain desensitized multi-dimensional information.

[0135] In this step, sensitive data identification rules are established in advance. The system will have some common de-identification rules pre-built in, such as regular expression recognition rules for ID card numbers, mobile phone numbers, email addresses, telephone numbers, academic certificate numbers, etc., and de-identification rules can also be added dynamically. Among these, a de-identification algorithm management system will be established, with a set of common algorithms pre-built in. These include hash de-identification, character masking, keyword replacement, and algorithms such as RSA and AES.

[0136] Establish a desensitization strategy template; select sensitive identification rules and desensitization algorithms. The system will match sensitive data according to the regular expressions in the identification rules. When sensitive data is identified, the system will dynamically desensitize the sensitive data according to the selected desensitization algorithm.

[0137] Configure a data masking strategy template for the API. You need to select a different template for each API interface. You can use a single template for all APIs, or create different templates for different interfaces. When an API is accessed, the returned data will be analyzed based on the API's corresponding data masking strategy template, and then the appropriate algorithm will be used to mask the sensitive data.

[0138] This application provides a user competency assessment method, particularly applicable to the innovative system for screening caregivers in elderly care institutions—the Fangxin Elderly Care System. This system aims to address the lack of effective screening mechanisms in the existing market, which can lead to inconsistent caregiver service quality and safety hazards. By integrating user information such as ID number, mobile phone number, and personal name, and combining it with social security databases, institutional records, and advanced evaluation algorithms, this application enables professional evaluation and screening of caregivers. This process not only ensures the rigor and standardization of the screening process but also fully respects and protects the privacy of caregivers. By adopting the Fangxin Elderly Care System, the service quality and safety of elderly care institutions can be significantly improved, providing more reliable service guarantees for residents and their families.

[0139] In this embodiment, constructing a scientific and comprehensive caregiver evaluation system is crucial for improving the reliability and consistency of elderly care service quality. Against this backdrop, "Reassuring Elderly Care" has emerged as an innovative solution. Based on the competence of elderly care personnel, combined with expert consultation and multi-dimensional evaluation methods, and under the premise of strictly protecting personal privacy, it integrates multi-source data such as social security data and institutional information through an authorization mechanism to achieve credible verification of caregiver backgrounds and qualifications. This mechanism helps improve the professional level of the caregiver team, thereby enhancing the satisfaction and trust of those in need of elderly care services.

[0140] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to this application.

[0141] Please also see Figure 3 This is a block diagram of a user competency assessment device provided in an embodiment of this application. The device includes: a verification module 301, a first query module 302, an assessment module 303, a first determination module 304, and a second determination module 305, wherein... The verification module 301 is used to verify the user's account in response to a query request input by the user; The first query module 302 is used to query the multi-dimensional information of the user to be queried in the query request after the verification is successful; Evaluation module 303 is used to evaluate the score of each preset indicator among multiple preset indicators based on the queried multi-dimensional information. The first determining module 304 is used to determine the talent competency index score of the user to be investigated based on the scores of all preset indicators. The second determining module 305 is used to determine the talent competency level of the user to be checked based on the talent competency index score.

[0142] Optionally, the verification module 301 includes: a request acquisition module 401, an authentication module 402, a deduction module 403, and a generation module 404, the structural block diagram of which is shown below. Figure 4 As shown, where, The request retrieval module 401 is used to retrieve the query request input by the user; Authentication module 402 is used to authenticate the user's permissions and account balance; Deduction module 403 is used to deduct the query fee after successful authentication; and The generation module 404 is used to generate blockchain evidence based on the query results after the query module completes the query.

[0143] Optionally, the query module is specifically used to query the multi-dimensional information of the user to be queried in the query request through the interface of the federated query gateway after the verification is successful. The multi-dimensional information includes: family background, social security participation, professional qualifications, education level, health status and whether there is a criminal record.

[0144] Optionally, the method further includes: a creation module for pre-creating a federated query gateway.

[0145] Optionally, the creation module includes: A unified module, used as a gateway to unify application programming interfaces (APIs); Add a module to carry a one-time identity token for each query, and The transformation module is used to convert each raw record retrieved by the query into a corresponding integer score via ETL. The encryption processing module is used to process indicators that require cross-source aggregation using the homomorphic encryption Paillier method. The privacy computing module is used to automatically inject Laplace noise according to a privacy budget when the query results contain individual characteristics (such as rare infectious diseases); The calculation module is used to calculate a verifiable delay function (VDF) proof of work for each query; The storage module is used to write the query serial number, the fractional hash returned by the data source, and the timestamp into the consortium blockchain.

[0146] Optionally, the evaluation module 303 includes: a scoring rule acquisition module 501 and an indicator scoring module 502, the structural block diagram of which is shown below. Figure 5 As shown, where, The scoring rule acquisition module 501 is used to acquire the scoring rules of preset indicators, which include: health dimension indicators, background dimension indicators, credit dimension indicators and basic qualification dimension indicators. The indicator scoring module 502 is used to evaluate the health dimension indicator, background dimension indicator, credit dimension indicator and basic qualification dimension indicator respectively based on the multi-dimensional information and according to the preset indicator scoring rules, so as to obtain the corresponding health dimension indicator score, background dimension indicator score, credit dimension indicator score and basic qualification dimension indicator score.

[0147] Optionally, the first determining module is specifically used to calculate the sum of scores for all preset indicators according to set weights to obtain the talent competency index score of the user to be investigated.

[0148] Optionally, the second determining module 305 includes: an indicator rule acquisition module 601 and a level determination module 602, the structural block diagram of which is shown below. Figure 6 As shown, where, The indicator rule acquisition module 601 is used to acquire preset talent competency indicator levels; The level determination module 602 is used to determine the talent competency level of the user to be checked based on the preset talent competency index level and the talent competency index score.

[0149] Optionally, the device further includes: a second query module 701, a behavior verification module 702, a trigger module 703, and a freeze module 704, the structural block diagram of which is shown below. Figure 7 As shown, where, The second query module 701 is used to obtain the query behavior data of the user during the query process when the first query module 302 queries the multi-dimensional information of the user to be queried in the query request. The behavior verification module 702 is used to verify the user's query behavior data; Trigger module 703 is used to trigger secondary verification when abnormal behavior of the user is detected; The freeze module 704 is used to freeze the user's query permissions when the secondary verification fails.

[0150] Optionally, after determining the talent competency level corresponding to the user to be queried, the device further includes: The sensitive data identification module is used to identify sensitive data in the multi-dimensional information of the user to be queried. The desensitization module is used to desensitize the identified sensitive data to obtain desensitized multi-dimensional information.

[0151] Optionally, embodiments of this application also provide an electronic device, including: It includes a processor, a memory; and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the user competence assessment method as described above.

[0152] Optionally, embodiments of this application also provide a readable storage medium storing a program or instructions that, when executed by a processor of an electronic device, implement the steps of the user competence assessment method described above.

[0153] Optionally, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed by a processor of an electronic device, implement the steps of the user competency assessment method described above.

[0154] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0155] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0156] Figure 8 This is a block diagram of an electronic device 800 provided in an embodiment of this application. For example, the electronic device 800 can be a mobile terminal or a server; in this embodiment, a mobile terminal is used as an example for explanation. For example, the electronic device 800 can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0157] Reference Figure 8 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0158] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0159] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0160] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0161] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0162] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0163] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0164] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0165] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0166] In the embodiments, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the various processes of the user competency assessment method embodiments shown above and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0167] In this embodiment, a readable storage medium is also provided, on which a program or instructions are stored. When executed by a processor of a processing electronic device, the program or instructions implement the steps of the user competency assessment method described above. The readable storage medium includes computer-readable storage media, such as ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices.

[0168] In this embodiment, a computer program product is also provided, including a computer program or instructions. When the computer program or instructions are executed by the processor 820 of the electronic device 800, the electronic device 800 performs the various processes of the above-described user competence assessment method embodiment and achieves the same technical effect. To avoid repetition, these will not be described again here.

[0169] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0170] Figure 9 This is a block diagram of an apparatus 900 for user competency assessment provided in an embodiment of this application. For example, apparatus 900 can be provided as a server. See also... Figure 9 The apparatus 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by memory 932 for storing instructions, such as application programs, that can be executed by the processing component 922. The application programs stored in memory 932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 922 is configured to execute instructions to perform the methods described above.

[0171] Device 900 may also include a power supply component 926 configured to perform power management of device 900, a wired or wireless network interface 950 configured to connect device 900 to a network, and an input / output (I / O) interface 958. Device 900 may operate on an operating system stored in memory 932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0172] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0173] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for assessing user competence, characterized in that, include: In response to a query request entered by the user, the user's account is verified; After successful verification, query the multi-dimensional information of the user to be queried in the query request; Based on the retrieved multi-dimensional information, evaluate the score of each preset indicator among multiple preset indicators; Based on the scores of all preset indicators, the talent competency index score of the user to be investigated is determined. The talent competency level of the user to be investigated is determined based on the talent competency index score.

2. The user competence assessment method according to claim 1, characterized in that, The process of verifying the user's account in response to a user-input query request includes: Get the query request input by the user; The user's permissions and account balance are authenticated; The query fee will be deducted after authentication is successful; and After the query is completed, a blockchain certificate is generated based on the query results.

3. The user competence assessment method according to claim 1, characterized in that, After successful verification, the process involves querying the multi-dimensional information of the user to be queried in the query request, including: After successful verification, the multi-dimensional information of the user to be searched in the query request is queried through the interface of the federated query gateway. The multi-dimensional information includes: family background, social security participation, professional qualifications, education level, health status and whether there is a criminal record.

4. The user competence assessment method according to claim 3, characterized in that, The method further includes: pre-establishing the interface of the federated query gateway in the following manner: A gateway for a unified application programming interface (API); Carry a one-time identity token with each query, and Each original record retrieved will be converted into a corresponding integer score via ETL; For metrics that require cross-source aggregation, the homomorphic encryption Paillier method is used for processing; When query results reveal individual characteristics, Laplace noise is automatically injected according to the privacy budget. Calculate a verifiable delay function (VDF) workload proof for each query; Write the query serial number, the fraction hash returned by the data source, and the timestamp into the consortium blockchain.

5. The user competence assessment method according to claim 1, characterized in that, The evaluation of the score for each of the multiple preset indicators based on the multi-dimensional information includes: The scoring rules for the preset indicators are obtained, including: health dimension indicators, background dimension indicators, credit dimension indicators and basic qualification dimension indicators. Based on the multi-dimensional information, and according to the scoring rules of the preset indicators, the health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators are evaluated respectively, and the corresponding scores for the health dimension indicators, background dimension indicators, credit dimension indicators, and basic qualification dimension indicators are obtained.

6. The user competence assessment method according to claim 1, characterized in that, The process of determining the talent competency index score of the user under investigation based on the scoring of all preset indicators includes: The sum of scores for all preset indicators is calculated according to the set weights to obtain the talent competency index score of the user to be investigated.

7. The user competence assessment method according to claim 1, characterized in that, The step of determining the talent competency level of the user to be queried based on the talent competency index score includes: Obtain preset talent competency index levels; Based on the preset talent competency index level and the talent competency index score, the talent competency level corresponding to the user to be searched is determined.

8. The user competency assessment method according to any one of claims 1 to 7, characterized in that, In the process of querying the multi-dimensional information of the user to be queried in the query request, the method further includes: Obtain the user's query behavior data during the query process; The user's query behavior data is detected; When abnormal user behavior is detected, secondary verification is triggered; If the secondary verification fails, the user's query privileges will be frozen.

9. The user competency assessment method according to any one of claims 1 to 7, characterized in that, In querying the multi-dimensional information of the user to be queried in the query request, the method further includes: Identify sensitive data in the multi-dimensional information of the user to be queried; The identified sensitive data is anonymized to obtain multi-dimensional information.

10. A user competency assessment device, characterized in that, include: The verification module is used to verify the user's account in response to a query request input by the user; The first query module is used to query the multi-dimensional information of the user to be queried in the query request after the verification is successful; The evaluation module is used to evaluate the score of each preset indicator among multiple preset indicators based on the queried multi-dimensional information. The first determining module is used to determine the talent competency index score of the user to be investigated based on the scores of all preset indicators. The second determining module is used to determine the talent competency level of the user to be checked based on the talent competency index score.

11. An electronic device, characterized in that, include: Including processor and memory; And a program or instructions stored on the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the user competence assessment method as described in any one of claims 1 to 9.

12. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor of an electronic device, implement the steps of the user competence assessment method as described in any one of claims 1 to 9.

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